With a few exceptions, preventive maintenance has been considered the most advanced and effective maintenance technique available for use by industrial and facility maintenance organizations. A Preventive Maintenance (PM) program is based on the assumption of a "fundamental cause-and-effect relationship between scheduled maintenance and operating reliability. This assumption was based on the intuitive belief that because mechanical parts wear out, the reliability of any equipment [is] directly related to operating age. It therefore followed that the more frequently equipment was overhauled, the better protected it was against the likelihood of failure. The only problem was in determining what age limit was necessary to assure reliable operation."
Nowlan and Heap reached the conclusion that, "a maintenance policy based exclusively on some maximum operating age would, no matter what the age limit, have little or no effect on the failure rate."
In separate independent studies, it was noted that a difference existed between the perceived and the intrinsic design life for the majority of equipment and components. In fact, it was discovered that in many cases equipment greatly exceeded the perceived or stated design life.
Reliability-Centered Maintenance (RCM) is the optimum mix of reactive, time- or interval-based, condition-based, and proactive maintenance practices. The basic application of each strategy is shown in Figure 1. These principal maintenance strategies, rather than being applied independently, are integrated to take advantage of their respective strengths in order to maximize facility and equipment reliability while minimizing life-cycle costs.
Preventive Maintenance (PM) assumes that failure probabilities can be determined statistically for individual machines and components, and parts can be replaced or adjustments can be performed in time to preclude failure. For example, a common practice has been to replace or renew bearings after so many operating hours assuming that bearing failure rate increases with time in service.
Figure 2, Bearing Life Scatter, shows the failure distribution of a group of thirty identical 6309 deep groove ball bearings installed on bearing life test machines and run to failure. The wide variation in bearing life is obvious and precludes the use of any effective time-based maintenance strategy.
Fortunately, computer advances in the 1990s have made it possible in many cases to identify the precursors of failure, quantify equipment condition, and schedule the appropriate repair with a higher degree of confidence than was possible when performing strictly interval-based maintenance relying upon usually erroneous estimates of when a component might fail. Also, it has been discovered recently that there are many different equipment failure characteristics, only a small number of which are age- or use-related. This new knowledge has increased the emphasis on Condition Monitoring (CM), often referred to as Condition-Based Maintenance, which has caused a reduction in the reliance upon time-based PM.
It should not be inferred from the above that all interval-based maintenance should be replaced by condition-based maintenance. In fact, interval-based maintenance is appropriate for those instances where abrasive, erosive, or corrosive wear takes place, material properties change due to fatigue, embrittlement, etc. and/or a clear correlation between age and functional reliability exists.
In addition, for those systems or components where no failure consequences in terms of mission, environment, safety, security, or Life-Cycle Cost (LCC) exist, maintenance should not be performed, i.e., the equipment should be run to failure and replaced.
The concept of RCM has been adopted across several government and industry operations as a strategy for performing maintenance. RCM applies maintenance strategies based on consequence and cost of failure. In addition, RCM seeks to minimize maintenance and improve reliability throughout the life-cycle by using proactive techniques such as improved design specifications, integration of condition monitoring in the commissioning process, and the Age Exploration (AE) process.
A. RCM Principles
The primary RCM principles are:
RCM is Function Oriented—RCM seeks to preserve system or equipment function, not just operability for operability's sake. Redundancy of function, through multiple pieces of equipment, improves functional reliability but increases life-cycle cost in terms of procurement and operating costs.
RCM is System Focused—RCM is more concerned with maintaining system function than with individual component function.
RCM is Reliability Centered—RCM treats failure statistics in an actuarial manner. The relationship between operating age and the failures experienced is important. RCM is not overly concerned with simple failure rate; it seeks to know the conditional probability of failure at specific ages (the probability that failure will occur in each given operating age bracket).
RCM Acknowledges Design Limitations—RCM objective is to maintain the inherent reliability of the equipment design, recognizing that changes in inherent reliability are the province of design rather than of maintenance. Maintenance can, at best, only achieve and maintain the level of reliability for equipment that was provided for by design. However, RCM recognizes that maintenance feedback can improve on the original design. In addition, RCM recognizes that a difference often exists between the perceived design life and the intrinsic or actual design life and addresses this through the Age Exploration (AE) process.
RCM Defines Failure as "Any Unsatisfactory Condition"—Therefore, failure may be either a loss of function (operation ceases) or a loss of acceptable quality (operation continues but impacts quality).
RCM Uses a Logic Tree to Screen Maintenance Tasks—This provides a consistent approach to the maintenance of all kinds of equipment.
RCM Tasks Must Be Applicable—The tasks must address the failure mode and consider the failure mode characteristics.
RCM Tasks Must Be Effective—The tasks must reduce the probability of failure and be cost-effective.
RCM Acknowledges Three Types of Maintenance Tasks—These tasks are time-directed (PM), condition-directed (CM), and failure finding (one of several aspects of Proactive Maintenance). Time-directed tasks are scheduled when appropriate. Condition-directed tasks are performed when conditions indicate they are needed. Failure-finding tasks detect hidden functions that have failed without giving evidence of pending failure. Additionally, performing no maintenance, Run-to-Failure, is a conscious decision and is acceptable for some equipment.
RCM is a Living System—RCM gathers data from the results achieved and feeds this data back to improve design and future maintenance. This feedback is an important part of the Proactive Maintenance element of the RCM program.
B. Types of RCM
There are several ways to conduct and implement an RCM program. The program can be based on rigorous Failure Modes and Effects Analysis (FMEA), complete with mathematically-calculated probabilities of failure based on design or historical data, intuition or common-sense, and/or experimental data and modeling. These approaches may be called Classical, Rigorous, Intuitive, Streamlined, or Abbreviated. Other terms sometimes used for these same approaches include Concise, Preventive Maintenance (PM) Optimization, Reliability Based, and Reliability Enhanced. All are applicable. The decision of what technique to use should be left to the end user and be based on:
- Consequences of failure
- Probability of failure
- Historical data available
- Risk tolerance
- Resource availability
Benefits: Classical or rigorous RCM provides the most knowledge and data concerning system functions, failure modes, and maintenance actions addressing functional failures of any of the RCM approaches. Rigorous RCM analysis is the method first proposed and documented by Nowlan and Heap and later modified by John Moubray, Anthony M. Smith, and others. In addition, this method should produce the most complete documentation of all the methods addressed here.
Concerns: Classical or rigorous RCM historically has been based primarily on the FMEA with little, if any, analysis of historical performance data. In addition, rigorous RCM analysis is extremely labor intensive and often postpones the implementation of obvious condition monitoring tasks.
- Applications: This approach should be limited to the following three situations:
- The consequences of failure result in catastrophic risk in terms of environment, health, or safety, and/or complete economic failure of the business unit.
- The resultant reliability and associated maintenance cost is still unacceptable after performing and implementing a streamlined type FMEA.
- The system/equipment is new to the organization and insufficient corporate maintenance and operational knowledge exists on function and functional failures.
Benefits: The intuitive approach identifies and implements the obvious, usually condition-based, tasks with minimal analysis. In addition, it culls or eliminates low value maintenance tasks based on historical data and Maintenance and Operations (M&O) personnel input. The intent is to minimize the initial analysis time in order to realize early-wins that help offset the cost of the FMEA and condition monitoring capabilities development.
Concerns: Reliance on historical records and personnel knowledge can introduce errors into the process that may lead to missing hidden failures where a low probability of occurrence exists. In addition, the intuitive process requires that at least one individual has a thorough understanding of the various condition monitoring technologies.
- Applications: This approach should be utilized when:
- The function of the system/equipment is well understood.
- Functional failure of the system/equipment will not result in loss of life or catastrophic impact on the environment or business unit.
- For these reasons, the streamlined or intuitive approach has been recommended for DOS, NASA, and NAVFAC facilities. In addition, a streamlined or intuitive approach has been successfully used in both discrete and continuous manufacturing facilities.
C. RCM Analysis
The RCM analysis should carefully consider and answer the following questions:
- What does the system or equipment do; what are the functions?
- What functional failures are likely to occur?
- What are the likely consequences of these functional failures?
- What can be done to reduce the probability of the failure(s), identify the onset of failure(s), or reduce the consequences of the failure(s)?
Answers to these four questions can be used with the decision logic tree depicted in Figure 3, Reliability-Centered Maintenance (RCM) Decision Logic Tree, to determine the maintenance approach for the equipment item or system.
Note that the analysis process as depicted in Figure 3 has only four possible outcomes:
- Perform Condition-Based actions (CM).
- Perform Interval (Time- or Cycle-) Based actions (PM).
- Determine that redesign will solve the problem and accept the failure risk, or determine that no maintenance action will reduce the probability of failure install redundancy.
- Perform no action and choose to repair following failure (Run-to-Failure).
Failure is the cessation of proper function or performance. RCM examines failure at several levels: the system level, sub-system level, component level, and sometimes even the parts level. The goal of an effective maintenance organization is to provide the required system performance at the lowest cost. This means that the maintenance approach must be based on a clear understanding of failure at each of the system levels. System components can be degraded or even failed and still not cause a system failure. A simple example is the failed headlamp on an automobile. That failed component has little effect on the overall system performance. Conversely, several degraded components may combine to cause the system to have failed, even though no individual component has itself failed.
System and System Boundary
A system is any user-defined group of components, equipment, or facilities that support an operational requirement. These operational requirements are defined by mission criticality or by environmental, health, safety, regulatory, quality, or other agency/business defined requirements. Most systems can be divided into unique sub-systems along user-defined boundaries. The boundaries are selected as a method of dividing a system into subsystems when its complexity makes an analysis by other means difficult:
- A system boundary or interface definition contains a description of the inputs and outputs that cross each boundary.
- The facility envelope is the physical barrier created by a building, enclosure, or other structure; e.g., a cooling tower or tank.
- Standardize on selecting boundaries. For example, a pump could include the first upstream/downstream isolation valve, the coupling, and associated gauges. The motor would include the electrical circuit from the load side of the motor control center but not the coupling.
The intent is to develop a series of modular FMEAs and assemble them as if they were Lego® Blocks and select the maintenance actions based on the consequences of risk determined by the criticality and probability factors defined in Tables 1 and 2 respectively.
Function and Functional Failure
The function defines the performance expectation and can have many elements. Elements include physical properties, operation performance including output tolerances, and time requirements such as continuous operation or limited required availability.
Functional failures are descriptions of the various ways in which a system or subsystem can fail to meet the functional requirements designed into the equipment. A system or subsystem that is operating in a degraded state but does not impact any of the requirements addressed in System and System Boundary, has not experienced a functional failure.
It is important to determine all of the functions of an item that are significant in a given operational context. By clearly defining the functions' non-performance, the functional failure becomes clearly defined. For example, it is not enough to define the function of a pump to move water. The function of the pump must be specific and defined in such terms flow rate, discharge pressure, vibration levels, B10 (L10) Life efficiency, etc. (Reliability HotWire)
Failure modes are equipment- and component-specific failures that result in the functional failure of the system or subsystem. For example, a machinery train composed of a motor and pump can fail catastrophically due to the complete failure of the windings, bearings, shaft, impeller, controller, or seals. In addition, a functional failure also occurs if the pump performance degrades such that there is insufficient discharge pressure or flow to meet operating requirements. These operational requirements should be considered when developing maintenance tasks.
Dominant failure modes are those failure modes responsible for a significant proportion of all the failures of the item. They are the most common modes of failure.
Not all failure modes or causes warrant preventive or conditioned based maintenance because the likelihood of their occurring is remote or their effect is inconsequential.
Reliability is the probability that an item will survive a given operating period, under specified operating conditions, without failure usually expressed as B10 (L10) Life and/or Mean Time to Failure (MTTF) or Mean Time Between Failure (MTBF). The conditional probability of failure measures the probability that an item entering a given age interval will fail during that interval. If the conditional probability of failure increases with age, the item shows wear-out characteristics. The conditional probability of failure reflects the overall adverse effect of age on reliability. It is not a measure of the change in an individual equipment item.
Failure rate or frequency plays a relatively minor role in maintenance programs because it is too simple a measure. Failure frequency is useful in making cost decisions and determining maintenance intervals, but it tells nothing about which maintenance tasks are appropriate or about the consequences of failure. A maintenance solution should be evaluated in terms of the safety, security, or economic consequences it is intended to prevent. A maintenance task must be applicable (i.e., prevent failures or ameliorate failure consequences) in order to be effective.
Conditional probability of failure (Pcond) curves fall into six basic types, as graphed (Pcond versus Time) in Figures 2-2 and 2-3, Random Conditional Probability of Failure Curves and Age Related Conditional Probability of Failure Curves. The percentage of equipment conforming to each of the six wear patterns as determined in three separate studies is also shown in both figures. (More)
The failure characteristics shown in Figures 4 and 5, Random Conditional Probability of Failure Curves, were first noted in the previously cited book, Reliability-Centered Maintenance. Follow-on studies in Sweden in 1973, and by the U.S. Navy in 1983, produced similar results. In these three studies, random failures accounted for 77–92% of the total failures and age related failure characteristics for the remaining 8–23%.
The basic difference between the failure patterns of complex and simple items has important implications for maintenance. Single-piece and simple items frequently demonstrate a direct relationship between reliability and age. This is particularly true where factors such as metal fatigue or mechanical wear are present or where the items are designed as consumables (short or predictable life spans). In these cases an age limit based on operating time or stress cycles may be effective in improving the overall reliability of the complex item of which they are a part.
Complex items frequently demonstrate some infant mortality, after which their failure probability increases gradually or remains constant. A marked wear-out age is not common. In many cases scheduled overhaul increases the overall failure rate by introducing a high infant mortality rate into an otherwise stable system.
Every equipment item has a characteristic that can be called resistance to or margin to failure. Using equipment subjects it to stress that can result in failure when the stress exceeds the resistance to failure. Figure 6, Preventing Failure, depicts this concept graphically. The figure shows that failures may be prevented or item life extended by:
- Decreasing the amount of stress applied to the item. The life of the item is extended for the period f0-f1 by the stress reduction shown.
- Increasing or restoring the item's resistance to failure. The life of the item is extended for the period f1-f2 by the resistance increase shown.
- Decreasing the rate of degradation of the item's resistance to or margin to failure. The life of the item is extended for the period f2-f3 by the decreased rate of resistance degradation shown.
Stress is dependent on use and may be highly variable. It may increase, decrease, or remain constant with use or time. A review of the failures of a large number of nominally identical simple items would disclose that the majority had about the same age at failure, subject to statistical variation, and that these failures occurred for the same reason. If one is considering preventive maintenance for some simple item and can find a way to measure its resistance to failure, he or she can use that information to help select a preventive task.
Adding excess material or changing the type of material that wears away or is consumed can increase resistance to failure or the rate of degradation. Excess strength may be provided to compensate for loss from corrosion or fatigue. The most common method of restoring resistance is by replacing the item. The resistance to failure of a simple item decreases with use or time (age), but a complex unit consists of hundreds of interacting simple items (parts) and has a considerable number of failure modes. In the complex case, the mechanisms of failure are the same, but they are operating on many simple component parts simultaneously and interactively so that failures no longer occur for the same reason at the same age. For these complex units, it is unlikely that one can design a maintenance task unless there are a few dominant or critical failure modes.
Failure Modes and Effects Analysis (FMEA)
FMEA is applied to each system, sub-system, and component identified in the boundary definition. For every function identified, there can be multiple failure modes. The FMEA addresses each system function (and, since failure is the loss of function, all possible failures) and the dominant failure modes associated with each failure, and then examines the consequences of the failure. What effect did the failure have on the mission or operation, the system, and on the machine?
Even though there are multiple failure modes, often the effects of the failure are the same or very similar in nature. That is, from a system function perspective, the outcome of any component failure may result in the system function being degraded.
Likewise, similar systems and machines will often have the same failure modes. However, the system use will determine the failure consequences. For example, the failure modes of a ball bearing will be the same regardless of the machine. However, the dominate failure mode will often change from machine to machine, the cause of the failure may change, and the effects of the failure will differ.
Figure 7, FMEA Worksheet, provides an example of a FMEA worksheet.
E. Criticality and Probability of Occurrence
Criticality assessment provides the means for quantifying how important a system function is relative to the identified Mission. Table 1, Criticality/Severity Categories, provides a method for ranking system criticality. This system, adapted from the automotive industry, provides 10 categories of Criticality/Severity. It is not the only method available. The categories can be expanded or contracted to produce a site-specific listing.
Table 1. Criticality/Severity Categories
|1||None||No reason to expect failure to have any effect on safety, health, environment, or mission.|
|2||Very Low||Minor disruption to facility function. Repair to failure can be accomplished during trouble call.|
|3||Low||Minor disruption to facility function. Repair to failure may be longer than trouble call but does not delay mission.|
|4||Low to Moderate||Moderate disruption to facility function. Some portion of mission may need to be reworked or process delayed.|
|5||Moderate||Moderate disruption to facility function. 100% of mission may need to be reworked or process delayed.|
|6||Moderate to High||Moderate disruption to facility function. Some portion of mission is lost. Moderate delay in restoring function.|
|7||High||High disruption to facility function. Some portion of mission is lost. Significant delay in restoring function.|
|8||Very High||High disruption to facility function. All of mission is lost. Significant delay in restoring function.|
|9||Hazard||Potential safety, health, or environmental issue. Failure will occur with warning.|
|10||Hazard||Potential safety, health, or environmental issue. Failure will occur without warning.|
The Probability of Occurrence (of Failure) is also based on work in the automotive industry. Table 2, Probability of Occurrence Categories, provides one possible method of quantifying the probability of failure. If there is historical data available, it will provide a powerful tool in establishing the ranking. If the historical data is not available, a ranking may be estimated based on experience with similar systems in the facilities area. The statistical ("Effect") column in Table 2 can be based on operating hours, day, cycles, or other unit that provides a consistent measurement approach. The statistical bases ("Comment") may be adjusted to account for local conditions. For example, one organization changed the statistical approach for ranking 1 through 5 to better reflect the number of cycles of the system being analyzed.
Table 2. Probability of Occurrence Categories
|1||1/10,000||Remote probability of occurrence; unreasonable to expect failure to occur.|
|2||1/5,000||Low failure rate. Similar to past design that has, in the past, had low failure rates for given volume/loads.|
|3||1/2,000||Low failure rate. Similar to past design that has, in the past, had low failure rates for given volume/loads.|
|4||1/1,000||Occasional failure rate. Similar to past design that has, in the past, had similar failure rates for given volume/loads.|
|5||1/500||Moderate failure rate. Similar to past design that has, in the past, had moderate failure rates for given volume/loads.|
|6||1/200||Moderate to high failure rate. Similar to past design that has, in the past, had moderate failure rates for given volume/loads.|
|7||1/100||High failure rate. Similar to past design that has, in the past, had high failure rates that has caused problems.|
|8||1/50||High failure rate. Similar to past design that has, in the past, had high failure rates that has caused problems.|
|9||1/20||Very High failure rate. Almost certain to cause problems.|
|10||1/10+||Very High failure rate. Almost certain to cause problems.|
F. RCM Implementation
There is no one set path for successfully implementing RCM because RCM is more than just performing a Failure Modes and Effects Analysis (FMEA), adopting condition monitoring techniques, and/or optimizing a maintenance and overhaul program through the application of an Age Exploration (AE) process. A successful RCM implementation process first must recognize what and where the source of return on investment (ROI) resides. The source(s) of ROI may be tangible and/or intangible. For the former, a quantifiable business case may be developed based on financial benefit (savings, cost avoidance, reduced Work in Progress (WIP) and/or reduced liability) to the organization while for the latter, the benefit may be unquantifiable (employee skills, morale, customer relations, etc.) In either case, a baseline and goal must be established through some mechanism such as internal or external benchmarking, which results in a defined gap between the "As-Is" and the "To-Be" state and the ROI identified for closing all or a portion of the gap.
Remember, caveat emptor. That is, RCM is not for everyone and very few organizations will benefit from implementing all elements of a classical RCM program. RCM like all tools/processes has an element of diminishing return. Not all the elements of RCM which are applicable to a nuclear power plant, the aircraft industry, and/or a 24/7 continuous process plant in a sold out condition, will be applicable to a batch process operation or a non-production facility. However, there are a few truths everyone should follow and there is no need to pilot or perform an FMEA analysis. They are:
Key performance indicators (aka metrics/performance indicators) are essential for establishing the baseline, goal, and the gap. Progress cannot be measured or sustained without KPIs. (See Section G-Key Performance Indicator (KPIs) Selection)
Thermography works for electrical distribution, boilers, couplings, roofing systems and building façades.
If your specifications for alignment, imbalance, motor circuit phase impedance, oil condition and cleanliness, and vibration are not quantified, the product you receive will have latent defects 80% of the time.
If you do not commission and check the sequence of operation of your equipment and buildings to a predetermined quantifiable specification, you will not get what you expect.
Pareto analysis is the best tool for determining where to start your RCM process. Look for the bottlenecks, the recurring failures, and follow the money.
RCM implementation in a team environment works better.
Failure modes for identical equipment are the same. It is only the consequence and probability of failure that changes.
The impact of poor water chemistry is underestimated in terms of energy consumption and life-cycle cost.
The majority of failures are random. Very few machines understand how a calendar works. Age Exploration can reveal hidden assets.
Celebrate and advertise your successes and address your failures. Credibility is a key to building support for long-term success.
G. Key Performance Indicators (KPIs) Selection
Significant thought must go into the process of selecting KPIs to support the maintenance program. The value of meaningful KPIs cannot be overstated; however the significance of KPIs that are inaccurate or inapplicable cannot be understated. First identify the goals and objectives of the organization because they will have an impact on the selection of KPIs at all levels of maintenance activity. KPIs that cannot possibly be obtained should not be chosen, and only those that may be controlled should be selected. Issues of concern should also be identified so that they will be considered in the selection of KPIs. All processes owners who are key to the implementation of the overall effort should have a self-selected metric to indicate goals and progress in meeting those goals. This will foster the acceptance of collecting data to support the KPIs and will also promote the use of the KPIs for continuous improvement. Also one must consider the capabilities of the organization to collect the data for KPIs, i.e. the process used for collecting and storing the data and the ease of extracting and reporting the KPIs. In doing this, the cost of obtaining data for the KPIs and the relative value they add to the overall program must be calculated. While advocating doing the right things within the maintenance program with life-cycle cost as a driver, the cost of the capturing supporting KPIs must also be watched closely.
1. Benchmark Selection
After selection of the appropriate KPIs is complete, benchmarks should be established. These characterize the organization's goals and/or progress points for using KPIs as a tool for maintenance optimization and continuous improvement. Benchmarks may be derived from the organizational goals and objectives or they may be selected from a survey performed with similar organizations. These benchmarks will be used as a target for growth and to evaluate risk associated with non-achievement of progress.
2. Utilization of KPIs
After benchmarks are established and data collection has begun, the information must be acted on in a timely manner to maintain continuity within all of the processes that are counting on KPIs as a performance enhancement tool. In order to take full advantage of the benefits of KPIs they should be displayed in public areas.
Relevant Codes and Standards
- Annual Book of ASTM Standards: Section: 5 Petroleum Products, Lubricants, and Fossil Fuels
- ASTM C1060 Standard Practice for Thermographic Inspection of Insulation Installations in Envelope Cavities of Frame Buildings
- ASTM C1153 Standard Practice for the Location of Wet Insulation in Roofing Systems Using Infrared Imaging
- ASTM E1186 Standard Practices for Air Leakage Site Detection in Building Envelopes and Air Barrier Systems
- ASTM E1316 Standard Terminology for Nondestructive Examinations
- Contamination Control for the Fluid Power Industry, Second Edition by E.C. Fitch and I.T. Hong. Silver Spring, MD: Pacific Scientific Company, 1990.
- Effective Machinery Measurement using Dynamic Signal Analyzers, Application Note 243-1, Hewlett Packard, 1990.
- The Fundamentals of Signal Analysis, Application Note 243, Hewlett Packard, 1991.
- Handbook of Lubrication and Tribology, several volumes, by Society of Tribologists and Lubrication Engineers (STLE).
- ISO 3945 Mechanical Vibration of Large Rotating Machines with Speed Range from 10-200 rev/s-Measurement and Evaluation of Vibration Severity in Situ
- ISO 6781 Thermal Insulation-Qualitative Detection of Thermal Irregularities in Building Envelopes-Infrared Method
- Laser Alignment Specification for New and Rebuilt Machinery and Equipment, Specification A 1.0-1993, General Motors, 1993.
- MIL-P-24534, Planned Maintenance System: Development of Maintenance Requirement Cards, Maintenance Index Pages, and Associated Documentation, U.S. Naval Sea Systems Command
- MIL-STD 2173 (AS), Reliability-Centered Maintenance Requirements for Naval Aircraft, Weapons Systems and Support Equipment, U.S. Naval Air Systems Command
- MIL-STD-2194 (SH), Infrared Thermal Imaging Survey Procedure for Electrical Equipment
- NAVAIR 00-25-403, Guidelines for the Naval Aviation Reliability Centered Maintenance Process, U.S. Naval Air System Command
- Reliability-Centered Maintenance Handbook, 59081-AB-GIB-010/MAINT, U.S. Naval Sea Systems Command
- SAE JA1O11, Evaluation Criteria for Reliability-Centered Maintenance (RCM) Processes, SAE International
- SAE JA1O12, A Guide to the Reliability-Centered Maintenance (RCM) Standard, SAE International
- NASA Facilities Engineering and Real Estate Division
- National Institute of Standards and Technology (NIST)
- American National Standards Institute (ANSI)
- Federal Facility Council (FFC)
- Institute of Electrical and Electronics Engineers (IEEE)
- International Organization for Standardization (ISO)
- Mechanical Failures Prevention Technology Society (MFPT)
- Reliability Information Analysis Center (RIAC) now operating as RMQSI Knowledge Center
- Society for Maintenance & Reliability Professionals (SMRP)
- Society of Automotive Engineers (SAE)
- Society of Reliability Engineers (SRE)
- Society of Tribologists and Lubrication Engineers (STLE)
- Complete Building Equipment Maintenance Desk Book, Second Edition by Sheldon J. Fuchs. Englewood, NJ: Prentice-Hall, 1992.
- Continuous Commissioning Guidebook for Federal Energy Managers DOE Federal Energy Management Program (FEMP), October 2002.
- Dependability Management-Part 3-1 1: Application Guide-Reliability Centered Maintenance by International Electrotechnical Commission. Document No. 56/651/EDIS.
- Maintainability: A Key to Effective Serviceability and Maintenance Management by B.S. Blanchard, D. Verma and E.L. Peterson. New York: John Wiley & Sons, Inc., 1995.
- Maintenance Technology—The Source for Reliability Solutions
- Operator/Manufacturer Scheduled Maintenance Development (MSG-3) by Air Transport Association (ATA). Washington, DC.
- Procedures for Performing a Failure Mode, Effects and Criticality Analysis by Department of Defense. Washington, DC, 1984. Military Standard MIL-STD 1629A, Notice 2.
- Reliability Centered Maintenance by Anthony M. Smith. New York: McGraw-Hill, 1993.
- Reliability-Centered Maintenance by F. Stanley Nowlan and Howard Heap. San Francisco: Dolby Access Press, 1978.
- Reliability-Centered Maintenance by F. Stanley Nowlan and Howard Heap. Washington, DC: Department of Defense, 1978. Report Number AD-A066579.
- Reliability-Centered Maintenance by John Moubray. Oxford: Butterworth-Heinemann Ltd., 1991.
- Reliability Centered Maintenance, A Practical Guide for Implementation by G. Zwingelstein. Paris: Hermes, 1996.
- Reliability Centered Maintenance Guide for Facilities and Collateral Equipment. National Aeronautics and Space Administration, 1996.
- Reliability-Centered Maintenance: Management and Engineering Methods by Ronald T. Anderson and Neri Lewis. London & New York: Elsevier Applied Science, 1990.
- Reliability, Maintainability, and Supportability Guidebook, Third Edition Society of Automotive Engineers, Inc., Warrendale, PA, 1995.
- Risk-Based Management: A Reliability-Centered Approach by Richard B. Jones. Houston, TX: Gulf Publishing Company, 1995.
- American Productivity & Quality Center (APQC)—Benchmark Resource
- Center for Risk and Reliability, University of Maryland
- Reliability & Maintainability Center, University of Tennessee
- PTC®, Inc.— Windchill FMEA (Failure Modes and Effects Analysis)